News for

Grading is complete and grades are posted. Final histograms

This Web site will be inactive until the Fall 2017 offering of the course.

A link to the digital audio videos talked about in class on 11/15/2015.

Python via Pylab, IPython, and IPython Notebook, worked well Fall 2014 (see also my recent Scipy paper). The first draft of the Python Basics tutorial is now posted. It includes examples of writing classes (object oridented programming).

The 'Other Course Materials' link opens up a directory listing. Check out the Python materials under my Signals and Systems for Dummies page.

A course of related interest Spring 2015 is Real-Time DSP, ECE 5655/4655-3, a three credit course on programming the ARM M4 Cortex (new for Spring 2014). (old TI-c6x platform (OMAP L138) using Code Composer Studio.)

Office Hours

T 3:05 to 4:15 PM and 7:05 to 8:00 PM,
or by appointment.
Phone 255-3500,

Learning Python

Python Basics (beta) a tutorial written in IPython Notebook

Link to Anaconda. This is the scientific Python I recommend.

An IDE I recommend is Pycharm Community Edition.

NumPy2MATLAB and IPython reference card.

The future of Jupyter Notebook is JupyterLab.

Obtaining Mathematica

EAS RATS and LATS Servers

Obtaining Mathematica

Mathematica is available across the campus due to the CU system wide site license. This system-site license also means that students may install their own copy on home computers as well. Some links of interest regarding the CU site license for Mathematica are: download and installation and support information.

Catalog Course Description

Study of linear discrete-time systems, linear difference equations, Z-transforms, discrete Fourier transform, fast Fourier transform, sensitivity discrete random processes, quantization effects and design-related concepts.
Prerequisite: ECE 3205 and ECE 3610, or equivalent
Offered: Fall (S)

Course Materials - Course Notes, m-Code

Course Syllabus as of 07:24 PM on Friday, August 19, 2016.

Intro Lecture as of 10:24 AM on Tuesday, August 23, 2016.

Lecture Notes

  • PDF file of Chapter 2 as of 09:33 PM on Sunday, August 21, 2016. (password given out in class on Wednesday)
  • PDF file of Chapter 3 as of 07:50 PM on Tuesday, August 23, 2016.
  • PDF file of Chapter 4 as of 08:14 AM on Tuesday, November 01, 2016.
  • PDF file of Chapter 5 as of 03:26 PM on Monday, October 31, 2016.
  • PDF file of Chapter 6 as of 03:26 PM on Monday, October 31, 2016.
  • PDF file of Chapter 7 as of 03:27 PM on Monday, October 31, 2016.
  • PDF file of Chapter 8 as of 09:42 PM on Saturday, December 10, 2016.
  • PDF file of Chapter 9 as of 09:43 PM on Saturday, December 10, 2016.

Other Course Materials

The DSP demo applications that I have used in class demos, are posted as ZIP files under the link Other Course Materials.

Support Materials for Sampling Theory

Lecture Videos - Streaming and Download

Fall 2016 Lectures as MP4 Movies

All video content is now MP4. The typical file size per lecture is about 300 MB, or less with the MP4. You may be able to stream them, but it is better to download and play from your file system.

The old videos from Fall 2015 will eventually be replaced by 2016 versions as they are created.

To directly download the lectures for playback at a later time, go to the lectures folder, right click, and download

Problem Sets with Solutions
Jupyter Example/Tutorial Notebooks

Updated Fall 2016 Jupyter Notebooks to Date

Fall 2015 Notebooks to Date

Fall 2014 Notebooks

Python Projects

Python-based projects making use of Numpy and Scipy will replace the older MATLAB projects for Fall 2014:

New Python Projects

Sample Exams with Solutions
  • TBD

Spring Related 2017 (cont.)

Statistical signal processing, ECE 4615/5615-3, is a good follow-on course for Modern DSP. The mathematics is intense, but the topics greatly enhance your signal and system analysis capabilities.